Multi-Kernel Object Tracking

In this paper, we present an object tracking algorithm for the low-frame-rate video in which objects have fast motion. The conventional mean-shift tracking fails in case the relocation of an object is large and its regions between the consecutive frames do not overlap. We provide a solution to this problem by using multiple kernels centered at the high motion areas. In addition, we improve the convergence properties of the mean-shift by integrating two likelihood terms, background and template similarities, in the iterative update mechanism. Our simulations prove the effectiveness of the proposed method

Thermo-visual feature fusion for object tracking using multiple spatiogram trackers

In this paper, we propose a framework that can efficiently combine features for robust tracking based on fusing the outputs of multiple spatiogram trackers. This is achieved without the exponential increase in storage and processing that other multimodal tracking approaches suffer from. The framework allows the features to be split arbitrarily between the trackers, as well as providing the flexibility to add, remove or dynamically weight features. We derive a mean-shift type algorithm for the framework that allows efficient object tracking with very low computational overhead. We especially target the fusion of thermal infrared and visible spectrum features as the most useful features for automated surveillance applications. Results are shown on multimodal video sequences clearly illustrating the benefits of combining multiple features using our framework

Online video streaming for human tracking based on weighted resampling particle filter

© 2018 The Authors. Published by Elsevier Ltd. This paper proposes a weighted resampling method for particle filter which is applied for human tracking on active camera. The proposed system consists of three major parts which are human detection, human tracking, and camera control. The codebook matching algorithm is used for extracting human region in human detection system, and the particle filter algorithm estimates the position of the human in every input image. The proposed system in this paper selects the particles with highly weighted value in resampling, because it provides higher accurate tracking features. Moreover, a proportional-integral-derivative controller (PID controller) controls the active camera by minimizing difference between center of image and the position of object obtained from particle filter. The proposed system also converts the position difference into pan-tilt speed to drive the active camera and keep the human in the field of view (FOV) camera. The intensity of image changes overtime while tracking human therefore the proposed system uses the Gaussian mixture model (GMM) to update the human feature model. As regards, the temporal occlusion problem is solved by feature similarity and the resampling particles. Also, the particle filter estimates the position of human in every input frames, thus the active camera drives smoothly. The robustness of the accurate tracking of the proposed system can be seen in the experimental results

Comparison of different integral histogram based tracking algorithms

Object tracking is an important subject in computer vision with a wide range of applications – security and surveillance, motion-based recognition, driver assistance systems, and human-computer interaction. The proliferation of high-powered computers, the availability of high quality and inexpensive video cameras, and the increasing need for automated video analysis have generated a great deal of interest in object tracking algorithms. Tracking is usually performed in the context of high-level applications that require the location and/or shape of the object in every frame. Research is being conducted in the development of object tracking algorithms over decades and a number of approaches have been proposed. These approaches differ from each other in object representation, feature selection, and modeling the shape and appearance of the object. Histogram-based tracking has been proved to be an efficient approach in many applications. Integral histogram is a novel method which allows the extraction of histograms of multiple rectangular regions in an image in a very efficient manner. A number of algorithms have used this function in their approaches in the recent years, which made an attempt to use the integral histogram in a more efficient manner. In this paper different algorithms which used this method as a part of their tracking function, are evaluated by comparing their tracking results and an effort is made to modify some of the algorithms for better performance. The sequences used for the tracking experiments are of gray scale (non-colored) and have significant shape and appearance variations for evaluating the performance of the algorithms. Extensive experimental results on these challenging sequences are presented, which demonstrate the tracking abilities of these algorithms

Object tracking in video sequence using the integral histogram

Tato práce se věnuje problematice sledování objektu v reálném čase. Sledovaný objektje definován oblastí ohraničujícího obdélníku. V práci je popsána problematika zpracováníobrazu a využití histogramu pro sledování objektu v reálném čase.Hlavním přínosem práce je rozšíření poskytnutého programu o sledování objektu v re-álném čase s měnící se velikostí ohraničujícího obdélníku. Dále je analyzováno chovánídetekce v různých scénářích. K tomu byly otestovány i různé výpočty vah. Programje napsán v C++ s využitím knihovny OpenCVThis thesis focuses on object tracking in real-time. Tracked object is defined by bounding rectangle. The thesis works on issue of image processing and using histogram for real-time object tracking. The main contribution of the work is the extension of the provided program to track object in real-time with changing bounding rectangle. Size of the rectangle is changing as the object moves closer of further from camera. Furthemore the detection behavior in different scenarios is analyzed. In addition, various weight calculations were tested. The program is written in C++ using OpenCV library.

Adaptive detection and tracking using multimodal information

This thesis describes work on fusing data from multiple sources of information, and focuses on two main areas: adaptive detection and adaptive object tracking in automated vision scenarios. The work on adaptive object detection explores a new paradigm in dynamic parameter selection, by selecting thresholds for object detection to maximise agreement between pairs of sources. Object tracking, a complementary technique to object detection, is also explored in a multi-source context and an efficient framework for robust tracking, termed the Spatiogram Bank tracker, is proposed as a means to overcome the difficulties of traditional histogram tracking. As well as performing theoretical analysis of the proposed methods, specific example applications are given for both the detection and the tracking aspects, using thermal infrared and visible spectrum video data, as well as other multi-modal information sources

Video content analysis for automated detection and tracking of humans in CCTV surveillance applications

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented

Single and multiple target tracking via hybrid mean shift/particle filter algorithms

This thesis is concerned with single and multiple target visual tracking algorithms and their application in the real world. While they are both powerful and general, one of the main challenges of tracking using particle filter-based algorithms is to manage the particle spread. Too wide a spread leads to dispersal of particles onto clutter, but limited spread may lead to difficulty when fast-moving objects and/or high-speed camera motion throw trackers away from their target(s). This thesis addresses the particle spread management problem. Three novel tracking algorithms are presented, each of which combines particle filtering and Kernel Mean Shift methods to produce more robust and accurate tracking. The first single target tracking algorithm, the Structured Octal Kernel Filter (SOK), combines Mean Shift (Comaniciu et al 2003) and Condensation (Isard and Blake 1998a). The spread of the particle set is handled by structurally placing the particles around the object, using eight particles arranged to cover the maximum area. Mean Shift is then applied to each particle to seek the global maxima. In effect, SOK uses intelligent switching between Mean Shift and particle filtering based on a confidence level. Though effective, it requires a threshold to be set and performs a somewhat inflexible search. The second single target tracking algorithm, the Kernel Annealed Mean Shift tracker (KAMS), uses an annealed particle filter (Deutscher et al 2000), but introduces a Mean Shift step to control particle spread. As a result, higher accuracy and robustness are achieved using fewer particles and annealing levels. Finally, KAMS is extended to create a multi-object tracking algorithm (MKAMS) by introducing an interaction filter to handle object collisions and occlusions. All three algorithms are compared experimentally with existing single/multiple object tracking algorithms. The evaluation procedure compares competing algorithms' robustness, accuracy and computational cost using both numerical measures and a novel application of McNemar's statistic. Results are presented on a wide variety of artificial and real image sequences